Taiwan has strictly followed infection control measures to prevent spread of coronavirus disease. Meanwhile, nationwide surveillance data revealed drastic decreases in influenza diagnoses in outpatient departments, positivity rates of clinical specimens, and confirmed severe cases during the first 12 weeks of 2020 compared with the same period of 2019.Compared with women, men tend to blame assault victims, exonerate perpetrators, and report higher levels of sexism and rape myths. The goal of the present study was to determine whether sexist beliefs mediate the established relationship between gender and rape myth acceptance in a sample of 626 college students. Results demonstrated that hostile sexism, complementary gender differentiation, and heterosexual intimacy mediated the relationship between gender and rape myth acceptance, suggesting that these attitudes play a role in victim blaming and have potential to inform the development of sexual assault prevention programs.Purpose To introduce the COVID-19 Reporting and Data System (CO-RADS) for standardized assessment of pulmonary involvement of COVID-19 on non-enhanced chest CT and report its initial interobserver agreement and performance. Methods The Dutch Radiological Society (NVvR) developed CO-RADS based on other efforts for standardization, such as Lung-RADS or BI-RADS. CO-RADS assesses the suspicion for pulmonary involvement of COVID-19 on a scale from 1 (very low) to 5 (very high). The system is meant to be used in patients presenting with moderate to severe symptoms of COVID-19. The system was evaluated using 105 chest CTs of patients admitted to the hospital with clinical suspicion of COVID-19 in whom RT-PCR was performed (62 +/- 16 years, 61 men, 53 with positive RT-PCR). Eight observers assessed the scans using CO-RADS. Fleiss' kappa was calculated, and scores of individual observers were compared to the median of the remaining seven observers. The resulting area under the receiver operating characteristics curve (AUC) was compared to results from RT-PCR and clinical diagnosis of COVID-19. Results There was absolute agreement among observers in 573 (68.2%) of 840 observations. Fleiss' kappa was 0.47 (95% confidence interval (CI) 0.45-0.47), with the highest kappa for CO-RADS categories 1 (0.58, 95% CI 0.54-0.62) and 5 (0.68, 95% CI 0.65-0.72). The average AUC was 0.91 (95% CI 0.85-0.97) for predicting RT-PCR outcome and 0.95 (95% CI 0.91-0.99) for clinical diagnosis. The false negative rate for CO-RADS 1 was 9/161 (5.6%, 95% CI 1.0-10%), and the false positive rate for CO-RADS 5 was 1/286 (0.3%, 95% CI 0-1.0%). Conclusions CO-RADS is a categorical assessment scheme for pulmonary involvement of COVID-19 on non-enhanced chest CT providing very good performance for predicting COVID-19 in patients with moderate to severe symptoms and has a substantial interobserver agreement, especially for categories 1 and 5.Background COVID-19 and pneumonia of other etiology share similar CT characteristics, contributing to the challenges in differentiating them with high accuracy. Purpose To establish and evaluate an artificial intelligence (AI) system in differentiating COVID-19 and other pneumonia on chest CT and assess radiologist performance without and with AI assistance. Methods 521 patients with positive RT-PCR for COVID-19 and abnormal chest CT findings were retrospectively identified from ten hospitals from January 2020 to April 2020. 665 patients with non-COVID-19 pneumonia and definite evidence of pneumonia on chest CT were retrospectively selected from three hospitals between 2017 and 2019. To classify COVID-19 versus other pneumonia for each patient, abnormal CT slices were input into the EfficientNet B4 deep neural network architecture after lung segmentation, followed by two-layer fully-connected neural network to pool slices together. Our final cohort of 1,186 patients (132,583 CT slices) was divided into training, validation and test sets in a 721 and equal ratio. Independent testing was performed by evaluating model performance on separate hospitals. Studies were blindly reviewed by six radiologists without and then with AI assistance. Results Our final model achieved a test accuracy of 96% (95% CI 90-98%), sensitivity 95% (95% CI 83-100%) and specificity of 96% (95% CI 88-99%) with Receiver Operating Characteristic (ROC) AUC of 0.95 and Precision-Recall (PR) AUC of 0.90. On independent testing, our model achieved an accuracy of 87% (95% CI 82-90%), sensitivity of 89% (95% CI 81-94%) and specificity of 86% (95% CI 80-90%) with ROC AUC of 0.90 and PR AUC of 0.87. Assisted by the models' probabilities, the radiologists achieved a higher average test accuracy (90% vs. 85%, Δ=5, p less then 0.001), sensitivity (88% vs. 79%, Δ=9, p less then 0.001) and specificity (91% vs. 88%, Δ=3, p=0.001). Conclusion AI assistance improved radiologists' performance in distinguishing COVID-19 from non-COVID-19 pneumonia on chest CT.Leishmaniasis is a vector-borne disease among the 10 most Neglected Tropical Diseases with diverse clinical manifestations caused by protozoan parasites of the Leishmania genus.  https://www.selleckchem.com/products/ITF2357(Givinostat).html  Around 80% of leishmaniasis cases are found in the Old World affecting populations mainly in low and middle-income countries. Its control relies mostly on chemotherapy which still presents many drawbacks. Natural products may offer an inexhaustible source of chemical diversity with therapeutic potential. Despite the lack of knowledge on traditional products with activity against Leishmania parasites, many reports describe the search for natural extracts and compounds with antileishmanial properties against promastigote and amastigote parasite forms. This review summarizes the research of 74 publications of the last decade (2008-2018) focused on the identification of endemic plant-derived products that are active against Old World Leishmania parasites responsible for cutaneous and visceral leishmaniasis. The present review combines data on antileishmanial activity of 423 plants species, belonging to 94 different families, including a large range of crude extracts which lead to the isolation of 86 active compounds. Most studied plants came from Asia and most promising plant families for antileishmanial activity were Asteraceae and Lamiaceae. From the chemical point of view, terpenoids were the most frequently isolated natural products. These studies suggest that natural products isolated from Old World flora are a rich source of new chemical scaffolds for future leishmaniasis treatment as well as for other Neglected Tropical Diseases warranting further investigation.